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Mapping the Emerging Curriculum for AI-Assisted Software Engineering via Syllabus Analysis

◐ medium · 2026-08-07ai-education, curriculum-design, software-engineering, instructional-design, higher-ed, generative-ai, ethics

Synthesis: This paper analyses 23 publicly available syllabi from upper-division, credit-bearing university courses that teach AI-assisted software development. The study identifies common curricular themes — prompt engineering, code review with AI, AI-augmented testing, ethical considerations — and maps how different institutions are defining this emerging subject area. Key findings include: a strong emphasis on critical evaluation of AI-generated code over pure generation speed, widespread integration of human-AI collaboration workflows, and substantial variation in how ethics and professional responsibility are addressed. The authors derive design guidance for future AI-assisted SE curricula, emphasising the need to balance tool fluency with foundational software engineering knowledge.

As Generative AI coding tools reshape professional software development, universities have begun designing courses to prepare students for AI-assisted development workflows. By analyzing the syllabi of these courses, we can gather empirical evidence about these courses, reveal how this emerging curricular area is being defined, and gain guidance for future curriculum design. We analyzed 23 publicly available syllabi and course materials of upper-division, credit-bearing courses that meet specific criteria, including explicitly addressing Generative AI in software engineering. Through iterative qualitative coding, we characterized courses' learning objectives, assessments, topics, and documented AI tools. Our analysis reveals commonalities and differences among these courses that allow rese

analyses 23 publicly available syllabi from upper-division, credit-bearing university courses that teach AI-assisted software development. The study identifies common curricular themes — prompt engineering, code review with AI, AI-augmented testing, ethical considerations — and maps how different institutions are defining this emerging subject area. Key findings include: a strong emphasis on critical evaluation of AI-generated code over pure generation speed, widespread integration of human-AI collaboration workflows, and substantial variation in how ethics and professional responsibility are addressed. The authors derive design guidance for future AI-assisted SE curricula, emphasising the need to balance tool fluency with foundational software engineering knowledge.

Connected Concepts

  • Curriculum Design
  • Instructional Design
  • Higher Ed
  • Accessible Learning
  • Human In The Loop AI
  • Formative Assessment
  • RCT
  • Active Learning

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    • Citation

    Geng, Francis et al. (2026). Mapping the Emerging Curriculum for AI-Assisted Software Engineering via Syllabus Analysis. arXiv:2608.05898.